Overview

Dataset statistics

Number of variables 35
Number of observations 37
Missing cells 526
Missing cells (%) 40.6%
Duplicate rows 0
Duplicate rows (%) 0.0%
Total size in memory 10.2 KiB
Average record size in memory 283.5 B

Variable types

Categorical 22
Numeric 11
Unsupported 1
Boolean 1

Alerts

Pre-Intervention Male (%) has constant value "97.56" Constant
Post-Intervention Male (%) has constant value "75.0" Constant
Post-Intervention Outsiders (%) has constant value "54.55" Constant
Driving Distance from CBD (km) is highly correlated with Other Sites Pre-Intervention Suicide and 1 other fields High correlation
Closest Psychiatric Hospital (km) is highly correlated with Hotspot Height(m) High correlation
Hotspot Height(m) is highly correlated with Closest Psychiatric Hospital (km) and 4 other fields High correlation
Restriction Height (m) is highly correlated with Year of Intervention and 6 other fields High correlation
Signs with Emergency Contact is highly correlated with Phones High correlation
Phones is highly correlated with Signs with Emergency Contact and 1 other fields High correlation
Pre-Intervention Years is highly correlated with Other Sites Post-Intervention Suicide and 1 other fields High correlation
Pre-Intervention Suicide is highly correlated with Yearly Pre-Intervention Rate and 4 other fields High correlation
Yearly Pre-Intervention Rate is highly correlated with Pre-Intervention Suicide and 6 other fields High correlation
Year of Intervention is highly correlated with Hotspot Height(m) and 4 other fields High correlation
Years of Installation is highly correlated with Hotspot Height(m) and 6 other fields High correlation
Post-Intervention Years is highly correlated with Other Sites Pre-Intervention Suicide and 3 other fields High correlation
Post-Intervention Suicide is highly correlated with Restriction Height (m) and 3 other fields High correlation
Yearly Post Intervention Rate is highly correlated with Restriction Height (m) and 7 other fields High correlation
Other Sites Pre-Intervention Suicide is highly correlated with Driving Distance from CBD (km) and 10 other fields High correlation
Other Sites Yearly Pre-Intervention Rate is highly correlated with Driving Distance from CBD (km) and 10 other fields High correlation
Other Sites Post-Intervention Suicide is highly correlated with Hotspot Height(m) and 11 other fields High correlation
Other Sites Yearly Post-Intervention Rate is highly correlated with Hotspot Height(m) and 11 other fields High correlation
Driving Distance from CBD (km) is highly correlated with Pre-Intervention Suicide and 3 other fields High correlation
Hotspot Height(m) is highly correlated with Year of Intervention and 3 other fields High correlation
Restriction Height (m) is highly correlated with Other Sites Pre-Intervention Suicide and 3 other fields High correlation
Signs with Emergency Contact is highly correlated with Phones High correlation
Phones is highly correlated with Signs with Emergency Contact High correlation
Blue Lights is highly correlated with Yearly Post Intervention Rate High correlation
Pre-Intervention Years is highly correlated with Other Sites Pre-Intervention Suicide and 3 other fields High correlation
Pre-Intervention Suicide is highly correlated with Driving Distance from CBD (km) and 5 other fields High correlation
Yearly Pre-Intervention Rate is highly correlated with Driving Distance from CBD (km) and 6 other fields High correlation
Year of Intervention is highly correlated with Hotspot Height(m) and 3 other fields High correlation
Years of Installation is highly correlated with Hotspot Height(m) and 6 other fields High correlation
Post-Intervention Suicide is highly correlated with Yearly Post Intervention Rate High correlation
Yearly Post Intervention Rate is highly correlated with Blue Lights and 2 other fields High correlation
Other Sites Pre-Intervention Suicide is highly correlated with Driving Distance from CBD (km) and 8 other fields High correlation
Other Sites Yearly Pre-Intervention Rate is highly correlated with Driving Distance from CBD (km) and 8 other fields High correlation
Other Sites Post-Intervention Suicide is highly correlated with Hotspot Height(m) and 10 other fields High correlation
Other Sites Yearly Post-Intervention Rate is highly correlated with Hotspot Height(m) and 9 other fields High correlation
Hotspot Height(m) is highly correlated with Year of Intervention and 3 other fields High correlation
Restriction Height (m) is highly correlated with Year of Intervention and 4 other fields High correlation
Signs with Emergency Contact is highly correlated with Phones High correlation
Phones is highly correlated with Signs with Emergency Contact High correlation
Pre-Intervention Suicide is highly correlated with Yearly Pre-Intervention Rate and 2 other fields High correlation
Yearly Pre-Intervention Rate is highly correlated with Pre-Intervention Suicide and 4 other fields High correlation
Year of Intervention is highly correlated with Hotspot Height(m) and 3 other fields High correlation
Years of Installation is highly correlated with Hotspot Height(m) and 4 other fields High correlation
Post-Intervention Years is highly correlated with Other Sites Pre-Intervention Suicide and 1 other fields High correlation
Post-Intervention Suicide is highly correlated with Yearly Post Intervention Rate and 2 other fields High correlation
Yearly Post Intervention Rate is highly correlated with Post-Intervention Suicide and 2 other fields High correlation
Other Sites Pre-Intervention Suicide is highly correlated with Restriction Height (m) and 9 other fields High correlation
Other Sites Yearly Pre-Intervention Rate is highly correlated with Restriction Height (m) and 9 other fields High correlation
Other Sites Post-Intervention Suicide is highly correlated with Hotspot Height(m) and 7 other fields High correlation
Other Sites Yearly Post-Intervention Rate is highly correlated with Hotspot Height(m) and 7 other fields High correlation
Hotspot Name is highly correlated with Country and 29 other fields High correlation
Country is highly correlated with Hotspot Name and 18 other fields High correlation
City is highly correlated with Hotspot Name and 23 other fields High correlation
Location Type is highly correlated with Hotspot Name and 9 other fields High correlation
Region is highly correlated with Hotspot Name and 10 other fields High correlation
Link is highly correlated with Hotspot Name and 14 other fields High correlation
Driving Distance from CBD (km) is highly correlated with Hotspot Name and 19 other fields High correlation
Closest Psychiatric Hospital (km) is highly correlated with Hotspot Name and 3 other fields High correlation
Suicide Method is highly correlated with Hotspot Name and 7 other fields High correlation
Hotspot Height(m) is highly correlated with Hotspot Name and 12 other fields High correlation
Restriction Type is highly correlated with Hotspot Name and 13 other fields High correlation
Restriction Height (m) is highly correlated with Hotspot Name and 10 other fields High correlation
Signs with Emergency Contact is highly correlated with Hotspot Name and 4 other fields High correlation
Phones is highly correlated with Hotspot Name and 7 other fields High correlation
CCTV is highly correlated with Hotspot Name and 5 other fields High correlation
Safety Staff is highly correlated with Hotspot Name and 3 other fields High correlation
Blue Lights is highly correlated with Hotspot Name and 7 other fields High correlation
Pre-Intervention Years is highly correlated with Hotspot Name and 11 other fields High correlation
Pre-Intervention Suicide is highly correlated with Hotspot Name and 18 other fields High correlation
Yearly Pre-Intervention Rate is highly correlated with Hotspot Name and 23 other fields High correlation
Year of Intervention is highly correlated with Hotspot Name and 14 other fields High correlation
Years of Installation is highly correlated with Hotspot Name and 12 other fields High correlation
Post-Intervention Years is highly correlated with Hotspot Name and 10 other fields High correlation
Post-Intervention Suicide is highly correlated with Hotspot Name and 16 other fields High correlation
Yearly Post Intervention Rate is highly correlated with Hotspot Name and 12 other fields High correlation
Displacement is highly correlated with Hotspot Name and 8 other fields High correlation
Other Sites Pre-Intervention Suicide is highly correlated with Hotspot Name and 20 other fields High correlation
Other Sites Yearly Pre-Intervention Rate is highly correlated with Hotspot Name and 20 other fields High correlation
Other Sites Post-Intervention Suicide is highly correlated with Hotspot Name and 12 other fields High correlation
Other Sites Yearly Post-Intervention Rate is highly correlated with Hotspot Name and 12 other fields High correlation
Distribution is highly correlated with Hotspot Name and 10 other fields High correlation
City has 16 (43.2%) missing values Missing
Location Type has 1 (2.7%) missing values Missing
Region has 28 (75.7%) missing values Missing
Link has 34 (91.9%) missing values Missing
Driving Distance from CBD (km) has 16 (43.2%) missing values Missing
Closest Psychiatric Hospital (km) has 21 (56.8%) missing values Missing
Hotspot Height(m) has 15 (40.5%) missing values Missing
Restriction Type has 7 (18.9%) missing values Missing
Restriction Height (m) has 10 (27.0%) missing values Missing
Pre-Intervention Male (%) has 36 (97.3%) missing values Missing
Pre-Intervention Outsiders (%) has 37 (100.0%) missing values Missing
Year of Intervention has 16 (43.2%) missing values Missing
Years of Installation has 27 (73.0%) missing values Missing
Post-Intervention Male (%) has 36 (97.3%) missing values Missing
Post-Intervention Outsiders (%) has 36 (97.3%) missing values Missing
Displacement has 30 (81.1%) missing values Missing
Other Sites Pre-Intervention Suicide has 32 (86.5%) missing values Missing
Other Sites Yearly Pre-Intervention Rate has 32 (86.5%) missing values Missing
Other Sites Post-Intervention Suicide has 34 (91.9%) missing values Missing
Other Sites Yearly Post-Intervention Rate has 34 (91.9%) missing values Missing
Distribution has 28 (75.7%) missing values Missing
Hotspot Name is uniformly distributed Uniform
City is uniformly distributed Uniform
Link is uniformly distributed Uniform
Other Sites Pre-Intervention Suicide is uniformly distributed Uniform
Other Sites Yearly Pre-Intervention Rate is uniformly distributed Uniform
Other Sites Post-Intervention Suicide is uniformly distributed Uniform
Other Sites Yearly Post-Intervention Rate is uniformly distributed Uniform
Hotspot Name has unique values Unique
Pre-Intervention Outsiders (%) is an unsupported type, check if it needs cleaning or further analysis Unsupported
Closest Psychiatric Hospital (km) has 1 (2.7%) zeros Zeros
Post-Intervention Suicide has 9 (24.3%) zeros Zeros
Yearly Post Intervention Rate has 9 (24.3%) zeros Zeros

Reproduction

Analysis started 2021-10-18 06:45:57.222275
Analysis finished 2021-10-18 06:46:24.724890
Duration 27.5 seconds
Software version pandas-profiling v3.1.0
Download configuration config.json

Variables

Hotspot Name
Categorical

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct 37
Distinct (%) 100.0%
Missing 0
Missing (%) 0.0%
Memory size 424.0 B
Grafton Bridge
 
1
Hammer Study - A
 
1
Hammer Study - F
 
1
Hammer Study - H
 
1
Hammer Study - K
 
1
Other values (32)
32 

Length

Max length 25
Median length 16
Mean length 15.40540541
Min length 9

Characters and Unicode

Total characters 0
Distinct characters 0
Distinct categories 0 ?
Distinct scripts 0 ?
Distinct blocks 0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 37 ?
Unique (%) 100.0%

Sample

1st row Grafton Bridge
2nd row Clifton Suspension Bridge
3rd row Hutson Bridge
4th row Beachy Head
5th row New Forest

Common Values

Value Count Frequency (%)
Grafton Bridge 1
 
2.7%
Hammer Study - A 1
 
2.7%
Hammer Study - F 1
 
2.7%
Hammer Study - H 1
 
2.7%
Hammer Study - K 1
 
2.7%
Hammer Study - M 1
 
2.7%
Hammer Study - B 1
 
2.7%
Hammer Study - C 1
 
2.7%
Hammer Study - E 1
 
2.7%
Hammer Study - L 1
 
2.7%
Other values (27) 27
73.0%

Length

2021-10-18T17:46:24.805710 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
study 20
18.3%
hammer 15
 
13.8%
15
 
13.8%
bridge 9
 
8.3%
head 2
 
1.8%
skyway 2
 
1.8%
the 2
 
1.8%
park 1
 
0.9%
new 1
 
0.9%
forest 1
 
0.9%
Other values (41) 41
37.6%

Most occurring characters

Value Count Frequency (%)
No values found.

Most occurring categories

Value Count Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value Count Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value Count Frequency (%)
No values found.

Most frequent character per block

Country
Categorical

HIGH CORRELATION

Distinct 9
Distinct (%) 24.3%
Missing 0
Missing (%) 0.0%
Memory size 424.0 B
Switzerland
17 
United States of America
5 
England
3 
Japan
3 
New Zealand
2 
Other values (4)
7 

Length

Max length 24
Median length 11
Mean length 11.24324324
Min length 5

Characters and Unicode

Total characters 0
Distinct characters 0
Distinct categories 0 ?
Distinct scripts 0 ?
Distinct blocks 0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 2.7%

Sample

1st row New Zealand
2nd row England
3rd row United States of America
4th row England
5th row England

Common Values

Value Count Frequency (%)
Switzerland 17
45.9%
United States of America 5
 
13.5%
England 3
 
8.1%
Japan 3
 
8.1%
New Zealand 2
 
5.4%
China 2
 
5.4%
Australia 2
 
5.4%
Canada 2
 
5.4%
South Korea 1
 
2.7%

Length

2021-10-18T17:46:24.923850 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-18T17:46:25.044752 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Value Count Frequency (%)
switzerland 17
30.9%
united 5
 
9.1%
states 5
 
9.1%
of 5
 
9.1%
america 5
 
9.1%
england 3
 
5.5%
japan 3
 
5.5%
new 2
 
3.6%
zealand 2
 
3.6%
china 2
 
3.6%
Other values (4) 6
 
10.9%

Most occurring characters

Value Count Frequency (%)
No values found.

Most occurring categories

Value Count Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value Count Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value Count Frequency (%)
No values found.

Most frequent character per block

City
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct 18
Distinct (%) 85.7%
Missing 16
Missing (%) 43.2%
Memory size 424.0 B
Tokyo
2 
Hong Kong
2 
St. Petersburg
2 
Augusta
 
1
Seoul
 
1
Other values (13)
13 

Length

Max length 14
Median length 7
Mean length 8.142857143
Min length 4

Characters and Unicode

Total characters 0
Distinct characters 0
Distinct categories 0 ?
Distinct scripts 0 ?
Distinct blocks 0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 15 ?
Unique (%) 71.4%

Sample

1st row Auckland
2nd row Bristol
3rd row Poughkeepsie
4th row East Sussex
5th row Hampshire

Common Values

Value Count Frequency (%)
Tokyo 2
 
5.4%
Hong Kong 2
 
5.4%
St. Petersburg 2
 
5.4%
Augusta 1
 
2.7%
Seoul 1
 
2.7%
Dunedin 1
 
2.7%
Toronto 1
 
2.7%
Bern 1
 
2.7%
Quebec 1
 
2.7%
Auckland 1
 
2.7%
Other values (8) 8
21.6%
(Missing) 16
43.2%

Length

2021-10-18T17:46:25.155678 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
tokyo 2
 
7.4%
kong 2
 
7.4%
st 2
 
7.4%
petersburg 2
 
7.4%
hong 2
 
7.4%
bristol 1
 
3.7%
poughkeepsie 1
 
3.7%
sussex 1
 
3.7%
east 1
 
3.7%
hampshire 1
 
3.7%
Other values (12) 12
44.4%

Most occurring characters

Value Count Frequency (%)
No values found.

Most occurring categories

Value Count Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value Count Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value Count Frequency (%)
No values found.

Most frequent character per block

Location Type
Categorical

HIGH CORRELATION
MISSING

Distinct 7
Distinct (%) 19.4%
Missing 1
Missing (%) 2.7%
Memory size 424.0 B
Bridge
23 
Railway
5 
Cliff
3 
Terrace
 
2
Forest
 
1
Other values (2)
 
2

Length

Max length 8
Median length 6
Mean length 6.222222222
Min length 5

Characters and Unicode

Total characters 0
Distinct characters 0
Distinct categories 0 ?
Distinct scripts 0 ?
Distinct blocks 0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 3 ?
Unique (%) 8.3%

Sample

1st row Bridge
2nd row Bridge
3rd row Bridge
4th row Cliff
5th row Forest

Common Values

Value Count Frequency (%)
Bridge 23
62.2%
Railway 5
 
13.5%
Cliff 3
 
8.1%
Terrace 2
 
5.4%
Forest 1
 
2.7%
Hospital 1
 
2.7%
Building 1
 
2.7%
(Missing) 1
 
2.7%

Length

2021-10-18T17:46:25.255582 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-18T17:46:25.325660 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Value Count Frequency (%)
bridge 23
63.9%
railway 5
 
13.9%
cliff 3
 
8.3%
terrace 2
 
5.6%
forest 1
 
2.8%
hospital 1
 
2.8%
building 1
 
2.8%

Most occurring characters

Value Count Frequency (%)
No values found.

Most occurring categories

Value Count Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value Count Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value Count Frequency (%)
No values found.

Most frequent character per block

Region
Categorical

HIGH CORRELATION
MISSING

Distinct 2
Distinct (%) 22.2%
Missing 28
Missing (%) 75.7%
Memory size 424.0 B
Metropolitan
6 
Regional
3 

Length

Max length 12
Median length 12
Mean length 10.66666667
Min length 8

Characters and Unicode

Total characters 0
Distinct characters 0
Distinct categories 0 ?
Distinct scripts 0 ?
Distinct blocks 0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Metropolitan
2nd row Regional
3rd row Metropolitan
4th row Regional
5th row Metropolitan

Common Values

Value Count Frequency (%)
Metropolitan 6
 
16.2%
Regional 3
 
8.1%
(Missing) 28
75.7%

Length

2021-10-18T17:46:25.425344 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-18T17:46:25.502953 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Value Count Frequency (%)
metropolitan 6
66.7%
regional 3
33.3%

Most occurring characters

Value Count Frequency (%)
No values found.

Most occurring categories

Value Count Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value Count Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value Count Frequency (%)
No values found.

Most frequent character per block

Link
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct 3
Distinct (%) 100.0%
Missing 34
Missing (%) 91.9%
Memory size 424.0 B
Two major central city roads
1 
Industrial and Suburban areas
1 
Two cities
1 

Length

Max length 29
Median length 29
Mean length 22.66666667
Min length 10

Characters and Unicode

Total characters 0
Distinct characters 0
Distinct categories 0 ?
Distinct scripts 0 ?
Distinct blocks 0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 3 ?
Unique (%) 100.0%

Sample

1st row Two major central city roads
2nd row Industrial and Suburban areas
3rd row Two cities

Common Values

Value Count Frequency (%)
Two major central city roads 1
 
2.7%
Industrial and Suburban areas 1
 
2.7%
Two cities 1
 
2.7%
(Missing) 34
91.9%

Length

2021-10-18T17:46:25.593601 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-18T17:46:25.693610 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Value Count Frequency (%)
two 2
18.2%
major 1
9.1%
central 1
9.1%
city 1
9.1%
roads 1
9.1%
industrial 1
9.1%
and 1
9.1%
suburban 1
9.1%
areas 1
9.1%
cities 1
9.1%

Most occurring characters

Value Count Frequency (%)
No values found.

Most occurring categories

Value Count Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value Count Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value Count Frequency (%)
No values found.

Most frequent character per block

Driving Distance from CBD (km)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 19
Distinct (%) 90.5%
Missing 16
Missing (%) 43.2%
Infinite 0
Infinite (%) 0.0%
Mean 5.347619048
Minimum 0.1
Maximum 25.3
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 424.0 B
2021-10-18T17:46:25.762858 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum 0.1
5-th percentile 0.6
Q1 0.8
median 2.6
Q3 5.5
95-th percentile 15.6
Maximum 25.3
Range 25.2
Interquartile range (IQR) 4.7

Descriptive statistics

Standard deviation 6.747415731
Coefficient of variation (CV) 1.261760733
Kurtosis 2.624183541
Mean 5.347619048
Median Absolute Deviation (MAD) 1.9
Skewness 1.750589344
Sum 112.3
Variance 45.52761905
Monotonicity Not monotonic
2021-10-18T17:46:25.892300 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
Value Count Frequency (%)
0.8 2
 
5.4%
0.7 2
 
5.4%
1.1 1
 
2.7%
3.1 1
 
2.7%
3.4 1
 
2.7%
0.1 1
 
2.7%
2.1 1
 
2.7%
0.6 1
 
2.7%
2.6 1
 
2.7%
1.5 1
 
2.7%
Other values (9) 9
24.3%
(Missing) 16
43.2%
Value Count Frequency (%)
0.1 1
2.7%
0.6 1
2.7%
0.7 2
5.4%
0.8 2
5.4%
1.1 1
2.7%
1.3 1
2.7%
1.5 1
2.7%
2.1 1
2.7%
2.6 1
2.7%
2.9 1
2.7%
Value Count Frequency (%)
25.3 1
2.7%
15.6 1
2.7%
15.1 1
2.7%
13.7 1
2.7%
10.4 1
2.7%
5.5 1
2.7%
5 1
2.7%
3.4 1
2.7%
3.1 1
2.7%
2.9 1
2.7%

Closest Psychiatric Hospital (km)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct 16
Distinct (%) 100.0%
Missing 21
Missing (%) 56.8%
Infinite 0
Infinite (%) 0.0%
Mean 4.8875
Minimum 0
Maximum 18.2
Zeros 1
Zeros (%) 2.7%
Negative 0
Negative (%) 0.0%
Memory size 424.0 B
2021-10-18T17:46:25.993876 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0.525
Q1 2.175
median 3.3
Q3 4.5
95-th percentile 18.05
Maximum 18.2
Range 18.2
Interquartile range (IQR) 2.325

Descriptive statistics

Standard deviation 5.373499791
Coefficient of variation (CV) 1.099437297
Kurtosis 3.719034645
Mean 4.8875
Median Absolute Deviation (MAD) 1.15
Skewness 2.11917065
Sum 78.2
Variance 28.8745
Monotonicity Not monotonic
2021-10-18T17:46:26.226100 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
Value Count Frequency (%)
3.5 1
 
2.7%
4.4 1
 
2.7%
4.2 1
 
2.7%
3 1
 
2.7%
2.1 1
 
2.7%
1.4 1
 
2.7%
4.8 1
 
2.7%
3.1 1
 
2.7%
0 1
 
2.7%
18.2 1
 
2.7%
Other values (6) 6
 
16.2%
(Missing) 21
56.8%
Value Count Frequency (%)
0 1
2.7%
0.7 1
2.7%
1.4 1
2.7%
2.1 1
2.7%
2.2 1
2.7%
2.8 1
2.7%
3 1
2.7%
3.1 1
2.7%
3.5 1
2.7%
4 1
2.7%
Value Count Frequency (%)
18.2 1
2.7%
18 1
2.7%
5.8 1
2.7%
4.8 1
2.7%
4.4 1
2.7%
4.2 1
2.7%
4 1
2.7%
3.5 1
2.7%
3.1 1
2.7%
3 1
2.7%

Suicide Method
Categorical

HIGH CORRELATION

Distinct 3
Distinct (%) 8.1%
Missing 0
Missing (%) 0.0%
Memory size 424.0 B
Jumping from height
30 
Jumping in front of moving object
5 
Carbon monoxide poisoning
 
2

Length

Max length 33
Median length 19
Mean length 21.21621622
Min length 19

Characters and Unicode

Total characters 0
Distinct characters 0
Distinct categories 0 ?
Distinct scripts 0 ?
Distinct blocks 0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Jumping from height
2nd row Jumping from height
3rd row Jumping from height
4th row Jumping from height
5th row Carbon monoxide poisoning

Common Values

Value Count Frequency (%)
Jumping from height 30
81.1%
Jumping in front of moving object 5
 
13.5%
Carbon monoxide poisoning 2
 
5.4%

Length

2021-10-18T17:46:26.354870 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-18T17:46:26.422873 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Value Count Frequency (%)
jumping 35
27.8%
from 30
23.8%
height 30
23.8%
in 5
 
4.0%
front 5
 
4.0%
of 5
 
4.0%
moving 5
 
4.0%
object 5
 
4.0%
carbon 2
 
1.6%
monoxide 2
 
1.6%

Most occurring characters

Value Count Frequency (%)
No values found.

Most occurring categories

Value Count Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value Count Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value Count Frequency (%)
No values found.

Most frequent character per block

Hotspot Height(m)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 20
Distinct (%) 90.9%
Missing 15
Missing (%) 40.5%
Infinite 0
Infinite (%) 0.0%
Mean 60.14090909
Minimum 23
Maximum 150
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 424.0 B
2021-10-18T17:46:26.494832 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum 23
5-th percentile 27.625
Q1 33.5
median 51
Q3 78.75
95-th percentile 104.9
Maximum 150
Range 127
Interquartile range (IQR) 45.25

Descriptive statistics

Standard deviation 32.78237998
Coefficient of variation (CV) 0.5450928574
Kurtosis 1.041138281
Mean 60.14090909
Median Absolute Deviation (MAD) 20.5
Skewness 1.119602921
Sum 1323.1
Variance 1074.684437
Monotonicity Not monotonic
2021-10-18T17:46:26.605710 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
Value Count Frequency (%)
47 2
 
5.4%
30 2
 
5.4%
68 1
 
2.7%
99 1
 
2.7%
31 1
 
2.7%
35 1
 
2.7%
55 1
 
2.7%
85 1
 
2.7%
33 1
 
2.7%
75 1
 
2.7%
Other values (10) 10
27.0%
(Missing) 15
40.5%
Value Count Frequency (%)
23 1
2.7%
27.5 1
2.7%
30 2
5.4%
31 1
2.7%
33 1
2.7%
35 1
2.7%
36.6 1
2.7%
40 1
2.7%
47 2
5.4%
55 1
2.7%
Value Count Frequency (%)
150 1
2.7%
105 1
2.7%
103 1
2.7%
99 1
2.7%
85 1
2.7%
80 1
2.7%
75 1
2.7%
68 1
2.7%
65 1
2.7%
58 1
2.7%

Restriction Type
Categorical

HIGH CORRELATION
MISSING

Distinct 8
Distinct (%) 26.7%
Missing 7
Missing (%) 18.9%
Memory size 424.0 B
Fence
18 
Mesh Net
5 
Platform Screen Doors
2 
Glass Barrier
 
1
Closed Road
 
1
Other values (3)
3 

Length

Max length 21
Median length 5
Mean length 7.866666667
Min length 5

Characters and Unicode

Total characters 0
Distinct characters 0
Distinct categories 0 ?
Distinct scripts 0 ?
Distinct blocks 0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 5 ?
Unique (%) 16.7%

Sample

1st row Glass Barrier
2nd row Fence
3rd row Closed Road
4th row Screen Doors
5th row Fence

Common Values

Value Count Frequency (%)
Fence 18
48.6%
Mesh Net 5
 
13.5%
Platform Screen Doors 2
 
5.4%
Glass Barrier 1
 
2.7%
Closed Road 1
 
2.7%
Screen Doors 1
 
2.7%
Window Guard Rail 1
 
2.7%
Locked Gate 1
 
2.7%
(Missing) 7
 
18.9%

Length

2021-10-18T17:46:26.734045 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-18T17:46:26.833311 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Value Count Frequency (%)
fence 18
40.0%
mesh 5
 
11.1%
net 5
 
11.1%
screen 3
 
6.7%
doors 3
 
6.7%
platform 2
 
4.4%
glass 1
 
2.2%
barrier 1
 
2.2%
closed 1
 
2.2%
road 1
 
2.2%
Other values (5) 5
 
11.1%

Most occurring characters

Value Count Frequency (%)
No values found.

Most occurring categories

Value Count Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value Count Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value Count Frequency (%)
No values found.

Most frequent character per block

Restriction Height (m)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 22
Distinct (%) 81.5%
Missing 10
Missing (%) 27.0%
Infinite 0
Infinite (%) 0.0%
Mean 2.698148148
Minimum 0.18
Maximum 7
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 424.0 B
2021-10-18T17:46:26.952875 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum 0.18
5-th percentile 0.74
Q1 1.75
median 2.4
Q3 3.275
95-th percentile 6.4
Maximum 7
Range 6.82
Interquartile range (IQR) 1.525

Descriptive statistics

Standard deviation 1.627811265
Coefficient of variation (CV) 0.603306852
Kurtosis 2.116728304
Mean 2.698148148
Median Absolute Deviation (MAD) 0.85
Skewness 1.331342092
Sum 72.85
Variance 2.649769516
Monotonicity Not monotonic
2021-10-18T17:46:27.099551 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
Value Count Frequency (%)
2.4 2
 
5.4%
4 2
 
5.4%
2.9 2
 
5.4%
7 2
 
5.4%
2 2
 
5.4%
3.25 1
 
2.7%
0.5 1
 
2.7%
1.7 1
 
2.7%
1.8 1
 
2.7%
2.65 1
 
2.7%
Other values (12) 12
32.4%
(Missing) 10
27.0%
Value Count Frequency (%)
0.18 1
2.7%
0.5 1
2.7%
1.3 1
2.7%
1.4 1
2.7%
1.49 1
2.7%
1.51 1
2.7%
1.7 1
2.7%
1.8 1
2.7%
1.9 1
2.7%
1.99 1
2.7%
Value Count Frequency (%)
7 2
5.4%
5 1
2.7%
4 2
5.4%
3.4 1
2.7%
3.3 1
2.7%
3.25 1
2.7%
2.9 2
5.4%
2.65 1
2.7%
2.58 1
2.7%
2.4 2
5.4%

Signs with Emergency Contact
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct 2
Distinct (%) 5.4%
Missing 0
Missing (%) 0.0%
Memory size 424.0 B
0
34 
1
 
3

Length

Max length 1
Median length 1
Mean length 1
Min length 1

Characters and Unicode

Total characters 0
Distinct characters 0
Distinct categories 0 ?
Distinct scripts 0 ?
Distinct blocks 0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0
2nd row 0
3rd row 1
4th row 0
5th row 1

Common Values

Value Count Frequency (%)
0 34
91.9%
1 3
 
8.1%

Length

2021-10-18T17:46:27.272964 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-18T17:46:27.402605 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Value Count Frequency (%)
0 34
91.9%
1 3
 
8.1%

Most occurring characters

Value Count Frequency (%)
No values found.

Most occurring categories

Value Count Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value Count Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value Count Frequency (%)
No values found.

Most frequent character per block

Phones
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct 2
Distinct (%) 5.4%
Missing 0
Missing (%) 0.0%
Memory size 424.0 B
0
31 
1
6 

Length

Max length 1
Median length 1
Mean length 1
Min length 1

Characters and Unicode

Total characters 0
Distinct characters 0
Distinct categories 0 ?
Distinct scripts 0 ?
Distinct blocks 0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0
2nd row 0
3rd row 1
4th row 0
5th row 1

Common Values

Value Count Frequency (%)
0 31
83.8%
1 6
 
16.2%

Length

2021-10-18T17:46:27.462800 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-18T17:46:27.535908 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Value Count Frequency (%)
0 31
83.8%
1 6
 
16.2%

Most occurring characters

Value Count Frequency (%)
No values found.

Most occurring categories

Value Count Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value Count Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value Count Frequency (%)
No values found.

Most frequent character per block

CCTV
Categorical

HIGH CORRELATION

Distinct 2
Distinct (%) 5.4%
Missing 0
Missing (%) 0.0%
Memory size 424.0 B
0
35 
1
 
2

Length

Max length 1
Median length 1
Mean length 1
Min length 1

Characters and Unicode

Total characters 0
Distinct characters 0
Distinct categories 0 ?
Distinct scripts 0 ?
Distinct blocks 0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0
2nd row 1
3rd row 0
4th row 0
5th row 0

Common Values

Value Count Frequency (%)
0 35
94.6%
1 2
 
5.4%

Length

2021-10-18T17:46:27.622937 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-18T17:46:27.706135 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Value Count Frequency (%)
0 35
94.6%
1 2
 
5.4%

Most occurring characters

Value Count Frequency (%)
No values found.

Most occurring categories

Value Count Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value Count Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value Count Frequency (%)
No values found.

Most frequent character per block

Safety Staff
Categorical

HIGH CORRELATION

Distinct 2
Distinct (%) 5.4%
Missing 0
Missing (%) 0.0%
Memory size 424.0 B
0
34 
1
 
3

Length

Max length 1
Median length 1
Mean length 1
Min length 1

Characters and Unicode

Total characters 0
Distinct characters 0
Distinct categories 0 ?
Distinct scripts 0 ?
Distinct blocks 0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0
2nd row 1
3rd row 0
4th row 0
5th row 0

Common Values

Value Count Frequency (%)
0 34
91.9%
1 3
 
8.1%

Length

2021-10-18T17:46:27.774405 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-18T17:46:27.853039 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Value Count Frequency (%)
0 34
91.9%
1 3
 
8.1%

Most occurring characters

Value Count Frequency (%)
No values found.

Most occurring categories

Value Count Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value Count Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value Count Frequency (%)
No values found.

Most frequent character per block

Blue Lights
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct 2
Distinct (%) 5.4%
Missing 0
Missing (%) 0.0%
Memory size 424.0 B
0
35 
1
 
2

Length

Max length 1
Median length 1
Mean length 1
Min length 1

Characters and Unicode

Total characters 0
Distinct characters 0
Distinct categories 0 ?
Distinct scripts 0 ?
Distinct blocks 0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0
2nd row 0
3rd row 0
4th row 0
5th row 0

Common Values

Value Count Frequency (%)
0 35
94.6%
1 2
 
5.4%

Length

2021-10-18T17:46:27.912784 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-18T17:46:28.005363 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Value Count Frequency (%)
0 35
94.6%
1 2
 
5.4%

Most occurring characters

Value Count Frequency (%)
No values found.

Most occurring categories

Value Count Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value Count Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value Count Frequency (%)
No values found.

Most frequent character per block

Pre-Intervention Years
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct 29
Distinct (%) 78.4%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 10.97918919
Minimum 1
Maximum 22
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 424.0 B
2021-10-18T17:46:28.086072 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum 1
5-th percentile 3
Q1 6.04
median 10.76
Q3 14.42
95-th percentile 21
Maximum 22
Range 21
Interquartile range (IQR) 8.38

Descriptive statistics

Standard deviation 5.660677413
Coefficient of variation (CV) 0.5155824638
Kurtosis -0.685280184
Mean 10.97918919
Median Absolute Deviation (MAD) 4.24
Skewness 0.2440777267
Sum 406.23
Variance 32.04326877
Monotonicity Not monotonic
2021-10-18T17:46:28.314354 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
Value Count Frequency (%)
16 2
 
5.4%
10 2
 
5.4%
4 2
 
5.4%
7 2
 
5.4%
3 2
 
5.4%
5 2
 
5.4%
13 2
 
5.4%
22 2
 
5.4%
6 1
 
2.7%
10.17 1
 
2.7%
Other values (19) 19
51.4%
Value Count Frequency (%)
1 1
2.7%
3 2
5.4%
4 2
5.4%
4.25 1
2.7%
5 2
5.4%
6 1
2.7%
6.04 1
2.7%
7 2
5.4%
7.31 1
2.7%
9.5 1
2.7%
Value Count Frequency (%)
22 2
5.4%
20.75 1
2.7%
20.5 1
2.7%
17.92 1
2.7%
16 2
5.4%
15.5 1
2.7%
15 1
2.7%
14.42 1
2.7%
14 1
2.7%
13.92 1
2.7%

Pre-Intervention Suicide
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct 29
Distinct (%) 78.4%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 45.27027027
Minimum 5
Maximum 221
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 424.0 B
2021-10-18T17:46:28.424513 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum 5
5-th percentile 5.8
Q1 13
median 25
Q3 53
95-th percentile 135.4
Maximum 221
Range 216
Interquartile range (IQR) 40

Descriptive statistics

Standard deviation 49.6334613
Coefficient of variation (CV) 1.096380936
Kurtosis 3.384142812
Mean 45.27027027
Median Absolute Deviation (MAD) 17
Skewness 1.845625008
Sum 1675
Variance 2463.48048
Monotonicity Not monotonic
2021-10-18T17:46:28.522939 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
Value Count Frequency (%)
25 3
 
8.1%
13 2
 
5.4%
29 2
 
5.4%
7 2
 
5.4%
14 2
 
5.4%
79 2
 
5.4%
5 2
 
5.4%
6 1
 
2.7%
9 1
 
2.7%
45 1
 
2.7%
Other values (19) 19
51.4%
Value Count Frequency (%)
5 2
5.4%
6 1
2.7%
7 2
5.4%
8 1
2.7%
9 1
2.7%
10 1
2.7%
11 1
2.7%
13 2
5.4%
14 2
5.4%
16 1
2.7%
Value Count Frequency (%)
221 1
2.7%
137 1
2.7%
135 1
2.7%
132 1
2.7%
127 1
2.7%
104 1
2.7%
79 2
5.4%
54 1
2.7%
53 1
2.7%
48 1
2.7%

Yearly Pre-Intervention Rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct 36
Distinct (%) 97.3%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 5.097027027
Minimum 0.38
Maximum 21.85
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 424.0 B
2021-10-18T17:46:28.622602 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum 0.38
5-th percentile 0.484
Q1 1.08
median 3.23
Q3 7.45
95-th percentile 16.38
Maximum 21.85
Range 21.47
Interquartile range (IQR) 6.37

Descriptive statistics

Standard deviation 5.310913745
Coefficient of variation (CV) 1.041963034
Kurtosis 2.333787426
Mean 5.097027027
Median Absolute Deviation (MAD) 2.36
Skewness 1.601921374
Sum 188.59
Variance 28.2058048
Monotonicity Not monotonic
2021-10-18T17:46:28.744973 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
Value Count Frequency (%)
0.73 2
 
5.4%
3.17 1
 
2.7%
8.2 1
 
2.7%
3.23 1
 
2.7%
0.87 1
 
2.7%
0.38 1
 
2.7%
3.31 1
 
2.7%
2.31 1
 
2.7%
1.17 1
 
2.7%
1.08 1
 
2.7%
Other values (26) 26
70.3%
Value Count Frequency (%)
0.38 1
2.7%
0.46 1
2.7%
0.49 1
2.7%
0.64 1
2.7%
0.73 2
5.4%
0.87 1
2.7%
0.9 1
2.7%
1.05 1
2.7%
1.08 1
2.7%
1.17 1
2.7%
Value Count Frequency (%)
21.85 1
2.7%
18.74 1
2.7%
15.79 1
2.7%
11.8 1
2.7%
11.29 1
2.7%
10 1
2.7%
9.45 1
2.7%
8.33 1
2.7%
8.2 1
2.7%
7.45 1
2.7%

Pre-Intervention Male (%)
Categorical

CONSTANT
MISSING
REJECTED

Distinct 1
Distinct (%) 100.0%
Missing 36
Missing (%) 97.3%
Memory size 424.0 B
97.56
1 

Length

Max length 5
Median length 5
Mean length 5
Min length 5

Characters and Unicode

Total characters 0
Distinct characters 0
Distinct categories 0 ?
Distinct scripts 0 ?
Distinct blocks 0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 100.0%

Sample

1st row 97.56

Common Values

Value Count Frequency (%)
97.56 1
 
2.7%
(Missing) 36
97.3%

Length

2021-10-18T17:46:28.853822 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-18T17:46:28.912874 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Value Count Frequency (%)
97.56 1
100.0%

Most occurring characters

Value Count Frequency (%)
No values found.

Most occurring categories

Value Count Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value Count Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value Count Frequency (%)
No values found.

Most frequent character per block

Pre-Intervention Outsiders (%)
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing 37
Missing (%) 100.0%
Memory size 424.0 B

Year of Intervention
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 13
Distinct (%) 61.9%
Missing 16
Missing (%) 43.2%
Infinite 0
Infinite (%) 0.0%
Mean 1999.619048
Minimum 1983
Maximum 2010
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 424.0 B
2021-10-18T17:46:28.964552 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum 1983
5-th percentile 1984
Q1 1998
median 2002
Q3 2004
95-th percentile 2008
Maximum 2010
Range 27
Interquartile range (IQR) 6

Descriptive statistics

Standard deviation 7.546364625
Coefficient of variation (CV) 0.003773901151
Kurtosis 0.5939018679
Mean 1999.619048
Median Absolute Deviation (MAD) 4
Skewness -1.066457095
Sum 41992
Variance 56.94761905
Monotonicity Not monotonic
2021-10-18T17:46:29.064557 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
Value Count Frequency (%)
2002 4
 
10.8%
1998 3
 
8.1%
1999 2
 
5.4%
2004 2
 
5.4%
2008 2
 
5.4%
1984 1
 
2.7%
2001 1
 
2.7%
1993 1
 
2.7%
1986 1
 
2.7%
2010 1
 
2.7%
Other values (3) 3
 
8.1%
(Missing) 16
43.2%
Value Count Frequency (%)
1983 1
 
2.7%
1984 1
 
2.7%
1986 1
 
2.7%
1993 1
 
2.7%
1998 3
8.1%
1999 2
5.4%
2001 1
 
2.7%
2002 4
10.8%
2004 2
5.4%
2005 1
 
2.7%
Value Count Frequency (%)
2010 1
 
2.7%
2008 2
5.4%
2006 1
 
2.7%
2005 1
 
2.7%
2004 2
5.4%
2002 4
10.8%
2001 1
 
2.7%
1999 2
5.4%
1998 3
8.1%
1993 1
 
2.7%

Years of Installation
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 5
Distinct (%) 50.0%
Missing 27
Missing (%) 73.0%
Memory size 424.0 B
1.0
3 
2.0
3 
3.0
2 
5.0
1 
4.0
1 

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 0
Distinct characters 0
Distinct categories 0 ?
Distinct scripts 0 ?
Distinct blocks 0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 2 ?
Unique (%) 20.0%

Sample

1st row 1.0
2nd row 1.0
3rd row 3.0
4th row 1.0
5th row 2.0

Common Values

Value Count Frequency (%)
1.0 3
 
8.1%
2.0 3
 
8.1%
3.0 2
 
5.4%
5.0 1
 
2.7%
4.0 1
 
2.7%
(Missing) 27
73.0%

Length

2021-10-18T17:46:29.175828 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-18T17:46:29.245781 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Value Count Frequency (%)
1.0 3
30.0%
2.0 3
30.0%
3.0 2
20.0%
5.0 1
 
10.0%
4.0 1
 
10.0%

Most occurring characters

Value Count Frequency (%)
No values found.

Most occurring categories

Value Count Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value Count Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value Count Frequency (%)
No values found.

Most frequent character per block

Post-Intervention Years
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct 25
Distinct (%) 67.6%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 5.854594595
Minimum 0.24
Maximum 22
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 424.0 B
2021-10-18T17:46:29.324429 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum 0.24
5-th percentile 1.68
Q1 2.83
median 4
Q3 6.5
95-th percentile 15.8
Maximum 22
Range 21.76
Interquartile range (IQR) 3.67

Descriptive statistics

Standard deviation 5.019542141
Coefficient of variation (CV) 0.8573680141
Kurtosis 2.601548603
Mean 5.854594595
Median Absolute Deviation (MAD) 1.5
Skewness 1.688554177
Sum 216.62
Variance 25.1958033
Monotonicity Not monotonic
2021-10-18T17:46:29.444841 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
Value Count Frequency (%)
5 5
 
13.5%
3 4
 
10.8%
4 3
 
8.1%
11 2
 
5.4%
2 2
 
5.4%
3.5 2
 
5.4%
2.4 1
 
2.7%
2.08 1
 
2.7%
0.24 1
 
2.7%
9.58 1
 
2.7%
Other values (15) 15
40.5%
Value Count Frequency (%)
0.24 1
 
2.7%
0.4 1
 
2.7%
2 2
5.4%
2.08 1
 
2.7%
2.2 1
 
2.7%
2.4 1
 
2.7%
2.5 1
 
2.7%
2.69 1
 
2.7%
2.83 1
 
2.7%
3 4
10.8%
Value Count Frequency (%)
22 1
2.7%
19 1
2.7%
15 1
2.7%
13 1
2.7%
11 2
5.4%
10.83 1
2.7%
10.08 1
2.7%
9.58 1
2.7%
6.5 1
2.7%
6.08 1
2.7%

Post-Intervention Suicide
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct 15
Distinct (%) 40.5%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 8.648648649
Minimum 0
Maximum 106
Zeros 9
Zeros (%) 24.3%
Negative 0
Negative (%) 0.0%
Memory size 424.0 B
2021-10-18T17:46:29.582770 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0
Q1 1
median 3
Q3 7
95-th percentile 26.6
Maximum 106
Range 106
Interquartile range (IQR) 6

Descriptive statistics

Standard deviation 19.11982133
Coefficient of variation (CV) 2.210729341
Kurtosis 19.76917811
Mean 8.648648649
Median Absolute Deviation (MAD) 3
Skewness 4.214379338
Sum 320
Variance 365.5675676
Monotonicity Not monotonic
2021-10-18T17:46:29.742940 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
Value Count Frequency (%)
0 9
24.3%
3 8
21.6%
1 5
13.5%
5 2
 
5.4%
16 2
 
5.4%
2 2
 
5.4%
20 1
 
2.7%
19 1
 
2.7%
13 1
 
2.7%
106 1
 
2.7%
Other values (5) 5
13.5%
Value Count Frequency (%)
0 9
24.3%
1 5
13.5%
2 2
 
5.4%
3 8
21.6%
5 2
 
5.4%
6 1
 
2.7%
7 1
 
2.7%
10 1
 
2.7%
11 1
 
2.7%
13 1
 
2.7%
Value Count Frequency (%)
106 1
2.7%
53 1
2.7%
20 1
2.7%
19 1
2.7%
16 2
5.4%
13 1
2.7%
11 1
2.7%
10 1
2.7%
7 1
2.7%
6 1
2.7%

Yearly Post Intervention Rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct 26
Distinct (%) 70.3%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 1.861081081
Minimum 0
Maximum 17.67
Zeros 9
Zeros (%) 24.3%
Negative 0
Negative (%) 0.0%
Memory size 424.0 B
2021-10-18T17:46:29.973486 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0
Q1 0.09
median 0.6
Q3 2.27
95-th percentile 6.966
Maximum 17.67
Range 17.67
Interquartile range (IQR) 2.18

Descriptive statistics

Standard deviation 3.361714
Coefficient of variation (CV) 1.806323236
Kurtosis 13.48355589
Mean 1.861081081
Median Absolute Deviation (MAD) 0.6
Skewness 3.342018156
Sum 68.86
Variance 11.30112102
Monotonicity Not monotonic
2021-10-18T17:46:30.155083 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
Value Count Frequency (%)
0 9
24.3%
0.31 2
 
5.4%
2.6 2
 
5.4%
0.99 2
 
5.4%
0.76 1
 
2.7%
17.67 1
 
2.7%
0.92 1
 
2.7%
0.4 1
 
2.7%
0.28 1
 
2.7%
1.06 1
 
2.7%
Other values (16) 16
43.2%
Value Count Frequency (%)
0 9
24.3%
0.09 1
 
2.7%
0.2 1
 
2.7%
0.25 1
 
2.7%
0.27 1
 
2.7%
0.28 1
 
2.7%
0.31 2
 
5.4%
0.4 1
 
2.7%
0.57 1
 
2.7%
0.6 1
 
2.7%
Value Count Frequency (%)
17.67 1
2.7%
8.15 1
2.7%
6.67 1
2.7%
6.33 1
2.7%
4.17 1
2.7%
4 1
2.7%
3.14 1
2.7%
2.6 2
5.4%
2.27 1
2.7%
1.67 1
2.7%

Post-Intervention Male (%)
Categorical

CONSTANT
MISSING
REJECTED

Distinct 1
Distinct (%) 100.0%
Missing 36
Missing (%) 97.3%
Memory size 424.0 B
75.0
1 

Length

Max length 4
Median length 4
Mean length 4
Min length 4

Characters and Unicode

Total characters 0
Distinct characters 0
Distinct categories 0 ?
Distinct scripts 0 ?
Distinct blocks 0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 100.0%

Sample

1st row 75.0

Common Values

Value Count Frequency (%)
75.0 1
 
2.7%
(Missing) 36
97.3%

Length

2021-10-18T17:46:30.398072 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-18T17:46:30.488188 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Value Count Frequency (%)
75.0 1
100.0%

Most occurring characters

Value Count Frequency (%)
No values found.

Most occurring categories

Value Count Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value Count Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value Count Frequency (%)
No values found.

Most frequent character per block

Post-Intervention Outsiders (%)
Categorical

CONSTANT
MISSING
REJECTED

Distinct 1
Distinct (%) 100.0%
Missing 36
Missing (%) 97.3%
Memory size 424.0 B
54.55
1 

Length

Max length 5
Median length 5
Mean length 5
Min length 5

Characters and Unicode

Total characters 0
Distinct characters 0
Distinct categories 0 ?
Distinct scripts 0 ?
Distinct blocks 0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 100.0%

Sample

1st row 54.55

Common Values

Value Count Frequency (%)
54.55 1
 
2.7%
(Missing) 36
97.3%

Length

2021-10-18T17:46:30.575144 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-18T17:46:30.674549 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Value Count Frequency (%)
54.55 1
100.0%

Most occurring characters

Value Count Frequency (%)
No values found.

Most occurring categories

Value Count Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value Count Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value Count Frequency (%)
No values found.

Most frequent character per block

Displacement
Boolean

HIGH CORRELATION
MISSING

Distinct 2
Distinct (%) 28.6%
Missing 30
Missing (%) 81.1%
Memory size 202.0 B
False
6 
True
 
1
(Missing)
30 
Value Count Frequency (%)
False 6
 
16.2%
True 1
 
2.7%
(Missing) 30
81.1%
2021-10-18T17:46:30.704813 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/

Other Sites Pre-Intervention Suicide
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct 5
Distinct (%) 100.0%
Missing 32
Missing (%) 86.5%
Memory size 424.0 B
7.0
1 
21.0
1 
20.0
1 
547.0
1 
4.0
1 

Length

Max length 5
Median length 4
Mean length 3.8
Min length 3

Characters and Unicode

Total characters 0
Distinct characters 0
Distinct categories 0 ?
Distinct scripts 0 ?
Distinct blocks 0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 5 ?
Unique (%) 100.0%

Sample

1st row 7.0
2nd row 21.0
3rd row 20.0
4th row 547.0
5th row 4.0

Common Values

Value Count Frequency (%)
7.0 1
 
2.7%
21.0 1
 
2.7%
20.0 1
 
2.7%
547.0 1
 
2.7%
4.0 1
 
2.7%
(Missing) 32
86.5%

Length

2021-10-18T17:46:30.782665 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-18T17:46:30.873102 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Value Count Frequency (%)
7.0 1
20.0%
21.0 1
20.0%
20.0 1
20.0%
547.0 1
20.0%
4.0 1
20.0%

Most occurring characters

Value Count Frequency (%)
No values found.

Most occurring categories

Value Count Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value Count Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value Count Frequency (%)
No values found.

Most frequent character per block

Other Sites Yearly Pre-Intervention Rate
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct 5
Distinct (%) 100.0%
Missing 32
Missing (%) 86.5%
Memory size 424.0 B
1.17
1 
5.25
1 
2.86
1 
49.73
1 
0.4
1 

Length

Max length 5
Median length 4
Mean length 4
Min length 3

Characters and Unicode

Total characters 0
Distinct characters 0
Distinct categories 0 ?
Distinct scripts 0 ?
Distinct blocks 0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 5 ?
Unique (%) 100.0%

Sample

1st row 1.17
2nd row 5.25
3rd row 2.86
4th row 49.73
5th row 0.4

Common Values

Value Count Frequency (%)
1.17 1
 
2.7%
5.25 1
 
2.7%
2.86 1
 
2.7%
49.73 1
 
2.7%
0.4 1
 
2.7%
(Missing) 32
86.5%

Length

2021-10-18T17:46:30.992923 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-18T17:46:31.073778 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Value Count Frequency (%)
1.17 1
20.0%
5.25 1
20.0%
2.86 1
20.0%
49.73 1
20.0%
0.4 1
20.0%

Most occurring characters

Value Count Frequency (%)
No values found.

Most occurring categories

Value Count Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value Count Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value Count Frequency (%)
No values found.

Most frequent character per block

Other Sites Post-Intervention Suicide
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct 3
Distinct (%) 100.0%
Missing 34
Missing (%) 91.9%
Memory size 424.0 B
87.0
1 
15.0
1 
621.0
1 

Length

Max length 5
Median length 4
Mean length 4.333333333
Min length 4

Characters and Unicode

Total characters 0
Distinct characters 0
Distinct categories 0 ?
Distinct scripts 0 ?
Distinct blocks 0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 3 ?
Unique (%) 100.0%

Sample

1st row 87.0
2nd row 15.0
3rd row 621.0

Common Values

Value Count Frequency (%)
87.0 1
 
2.7%
15.0 1
 
2.7%
621.0 1
 
2.7%
(Missing) 34
91.9%

Length

2021-10-18T17:46:31.163260 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-18T17:46:31.235409 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Value Count Frequency (%)
87.0 1
33.3%
15.0 1
33.3%
621.0 1
33.3%

Most occurring characters

Value Count Frequency (%)
No values found.

Most occurring categories

Value Count Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value Count Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value Count Frequency (%)
No values found.

Most frequent character per block

Other Sites Yearly Post-Intervention Rate
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct 3
Distinct (%) 100.0%
Missing 34
Missing (%) 91.9%
Memory size 424.0 B
15.82
1 
4.2
1 
65.68
1 

Length

Max length 5
Median length 5
Mean length 4.333333333
Min length 3

Characters and Unicode

Total characters 0
Distinct characters 0
Distinct categories 0 ?
Distinct scripts 0 ?
Distinct blocks 0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 3 ?
Unique (%) 100.0%

Sample

1st row 15.82
2nd row 4.2
3rd row 65.68

Common Values

Value Count Frequency (%)
15.82 1
 
2.7%
4.2 1
 
2.7%
65.68 1
 
2.7%
(Missing) 34
91.9%

Length

2021-10-18T17:46:31.352815 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-18T17:46:31.435359 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Value Count Frequency (%)
15.82 1
33.3%
4.2 1
33.3%
65.68 1
33.3%

Most occurring characters

Value Count Frequency (%)
No values found.

Most occurring categories

Value Count Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value Count Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value Count Frequency (%)
No values found.

Most frequent character per block

Distribution
Categorical

HIGH CORRELATION
MISSING

Distinct 2
Distinct (%) 22.2%
Missing 28
Missing (%) 75.7%
Memory size 424.0 B
Poisson
7 
Chi Square
2 

Length

Max length 10
Median length 7
Mean length 7.666666667
Min length 7

Characters and Unicode

Total characters 0
Distinct characters 0
Distinct categories 0 ?
Distinct scripts 0 ?
Distinct blocks 0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Chi Square
2nd row Chi Square
3rd row Poisson
4th row Poisson
5th row Poisson

Common Values

Value Count Frequency (%)
Poisson 7
 
18.9%
Chi Square 2
 
5.4%
(Missing) 28
75.7%

Length

2021-10-18T17:46:31.512774 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-18T17:46:31.583038 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Value Count Frequency (%)
poisson 7
63.6%
chi 2
 
18.2%
square 2
 
18.2%

Most occurring characters

Value Count Frequency (%)
No values found.

Most occurring categories

Value Count Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value Count Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value Count Frequency (%)
No values found.

Most frequent character per block

Interactions

2021-10-18T17:46:20.792484 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:01.310088 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:02.894888 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:04.985121 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:07.215004 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:09.350837 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:11.293335 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:13.253679 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:15.399343 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:17.195047 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:18.865362 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:20.962976 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:01.443680 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:03.044466 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:05.175807 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:07.419334 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:09.500621 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:11.503977 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:13.431931 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:15.559353 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:17.474994 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:19.048347 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:21.084590 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:01.588975 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:03.238642 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:05.372028 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:07.598001 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:09.687767 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:11.652087 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:13.748852 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:15.711446 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:17.605197 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:19.215014 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:21.214568 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:01.706910 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:03.410006 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:05.565496 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:07.824953 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:09.954121 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:11.752042 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:13.987962 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:15.920192 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:17.767375 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:19.375672 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:21.364183 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:01.824952 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:03.530164 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:05.752510 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:08.036017 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:10.119897 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:11.924904 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:14.169860 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:16.104760 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:17.954778 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:19.504719 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:21.482889 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:01.978868 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:03.756588 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:05.974315 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:08.237136 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:10.273087 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:12.131295 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:14.332843 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:16.272919 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:18.047564 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:19.716477 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:21.596849 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:02.091569 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:03.958974 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:06.266305 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:08.420692 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:10.447415 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:12.269143 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:14.499547 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:16.415030 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:18.185620 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:19.885226 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:21.715868 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:02.253828 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:04.130166 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:06.468583 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:08.568455 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:10.619706 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:12.472121 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:14.651728 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:16.569226 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:18.324826 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:20.014835 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:21.832925 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:02.461791 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:04.316905 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:06.655886 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:08.816238 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:10.732523 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:12.668097 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:14.798692 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:16.713189 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:18.435318 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:20.213121 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:21.983149 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:02.611542 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:04.583549 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:06.843646 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:08.976870 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:10.877222 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:12.909812 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:15.151005 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:16.884004 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:18.575205 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:20.344370 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:22.145461 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:02.753458 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:04.763712 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:07.026863 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:09.181755 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:11.088459 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:13.106737 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:15.268790 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:17.034775 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:18.714195 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
2021-10-18T17:46:20.516924 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-10-18T17:46:31.673243 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-18T17:46:32.056360 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-18T17:46:32.505693 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-18T17:46:32.875769 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-18T17:46:22.434753 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-18T17:46:23.457026 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-10-18T17:46:24.007262 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-10-18T17:46:24.507228 image/svg+xml Matplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Hotspot Name Country City Location Type Region Link Driving Distance from CBD (km) Closest Psychiatric Hospital (km) Suicide Method Hotspot Height(m) Restriction Type Restriction Height (m) Signs with Emergency Contact Phones CCTV Safety Staff Blue Lights Pre-Intervention Years Pre-Intervention Suicide Yearly Pre-Intervention Rate Pre-Intervention Male (%) Pre-Intervention Outsiders (%) Year of Intervention Years of Installation Post-Intervention Years Post-Intervention Suicide Yearly Post Intervention Rate Post-Intervention Male (%) Post-Intervention Outsiders (%) Displacement Other Sites Pre-Intervention Suicide Other Sites Yearly Pre-Intervention Rate Other Sites Post-Intervention Suicide Other Sites Yearly Post-Intervention Rate Distribution
0 Grafton Bridge New Zealand Auckland Bridge Metropolitan Two major central city roads 1.1 NaN Jumping from height 80.0 Glass Barrier NaN 0 0 0 0 0 6.0 19 3.17 NaN NaN 2002.0 1.0 4.0 0 0.00 NaN NaN Yes 7.0 1.17 NaN NaN Chi Square
1 Clifton Suspension Bridge England Bristol Bridge NaN NaN NaN NaN Jumping from height NaN Fence 2.0 0 0 1 1 0 5.0 41 8.20 97.56 NaN 1998.0 NaN 5.0 20 4.00 75.0 NaN NaN NaN NaN NaN NaN NaN
2 Hutson Bridge United States of America Poughkeepsie Bridge NaN NaN NaN NaN Jumping from height NaN NaN NaN 1 1 0 0 0 1.0 5 5.00 NaN NaN 1984.0 NaN 2.2 5 2.27 NaN NaN NaN NaN NaN NaN NaN NaN
3 Beachy Head England East Sussex Cliff Regional NaN 25.3 NaN Jumping from height 105.0 Closed Road NaN 0 0 0 0 0 14.0 221 15.79 NaN NaN 2001.0 1.0 0.4 0 0.00 NaN NaN NaN NaN NaN NaN NaN Chi Square
4 New Forest England Hampshire Forest NaN NaN NaN NaN Carbon monoxide poisoning NaN NaN NaN 1 1 0 0 0 10.0 47 4.70 NaN NaN 1998.0 NaN 3.0 5 1.67 NaN NaN NaN NaN NaN NaN NaN NaN
5 Law Study China Hong Kong Railway NaN NaN NaN NaN Jumping in front of moving object NaN Screen Doors 2.0 0 0 0 0 0 5.0 29 5.80 NaN NaN 2002.0 3.0 5.0 3 0.60 NaN NaN NaN NaN NaN NaN NaN NaN
6 Gateway Bridge Australia Brisbane Bridge Metropolitan Industrial and Suburban areas 13.7 NaN Jumping from height 65.0 Fence 3.3 0 0 0 0 0 4.0 22 5.50 NaN NaN 1993.0 1.0 19.0 16 0.84 NaN NaN No 21.0 5.25 87.0 15.82 Poisson
7 Ellington Bridge United States of America Washington DC Bridge NaN NaN NaN NaN Jumping from height NaN Fence 2.4 0 0 0 0 0 7.0 25 3.57 NaN NaN 1986.0 NaN 5.0 1 0.20 NaN NaN NaN 20.0 2.86 15.0 4.20 NaN
8 Sunshine Skyway Bridge United States of America St. Petersburg Bridge NaN NaN NaN NaN Jumping from height NaN NaN NaN 0 1 0 1 0 3.0 25 8.33 NaN NaN 1999.0 2.0 3.0 19 6.33 NaN NaN NaN NaN NaN NaN NaN NaN
9 The Gap Park Australia Sydney Cliff Regional NaN 10.4 NaN Jumping from height 30.0 Fence 1.3 1 1 1 0 0 10.6 79 7.45 NaN NaN 2010.0 2.0 2.4 16 6.67 NaN NaN NaN NaN NaN NaN NaN Poisson

Last rows

Hotspot Name Country City Location Type Region Link Driving Distance from CBD (km) Closest Psychiatric Hospital (km) Suicide Method Hotspot Height(m) Restriction Type Restriction Height (m) Signs with Emergency Contact Phones CCTV Safety Staff Blue Lights Pre-Intervention Years Pre-Intervention Suicide Yearly Pre-Intervention Rate Pre-Intervention Male (%) Pre-Intervention Outsiders (%) Year of Intervention Years of Installation Post-Intervention Years Post-Intervention Suicide Yearly Post Intervention Rate Post-Intervention Male (%) Post-Intervention Outsiders (%) Displacement Other Sites Pre-Intervention Suicide Other Sites Yearly Pre-Intervention Rate Other Sites Post-Intervention Suicide Other Sites Yearly Post-Intervention Rate Distribution
27 Hammer Study - E Switzerland NaN Bridge NaN NaN 2.6 4.8 Jumping from height 85.0 Fence 1.80 0 0 0 0 0 20.50 24 1.17 NaN NaN NaN NaN 3.50 2 0.57 NaN NaN NaN NaN NaN NaN NaN NaN
28 Hammer Study - L Switzerland NaN Building NaN NaN 0.6 1.4 Jumping from height 30.0 Fence 2.40 0 0 0 0 0 10.17 11 1.08 NaN NaN NaN NaN 2.83 3 1.06 NaN NaN NaN NaN NaN NaN NaN NaN
29 Hammer Study - O Switzerland NaN Bridge NaN NaN 2.1 2.1 Jumping from height 55.0 Fence 1.70 0 0 0 0 0 13.17 6 0.46 NaN NaN NaN NaN 10.83 3 0.28 NaN NaN NaN NaN NaN NaN NaN NaN
30 Hammer Study - J Switzerland NaN Terrace NaN NaN 0.8 3.0 Jumping from height 35.0 Mesh Net 7.00 0 0 0 0 0 4.00 9 2.25 NaN NaN NaN NaN 15.00 0 0.00 NaN NaN NaN NaN NaN NaN NaN NaN
31 Hammer Study - G Switzerland NaN Bridge NaN NaN 0.1 4.2 Jumping from height 31.0 Mesh Net 4.00 0 0 0 0 0 15.50 14 0.90 NaN NaN NaN NaN 2.50 1 0.40 NaN NaN NaN NaN NaN NaN NaN NaN
32 Hammer Study - I Switzerland NaN Bridge NaN NaN 3.4 4.4 Jumping from height 99.0 Mesh Net 4.00 0 0 0 0 0 20.75 25 1.20 NaN NaN NaN NaN 3.25 3 0.92 NaN NaN NaN NaN NaN NaN NaN NaN
33 Hammer Study - N Switzerland NaN Bridge NaN NaN 15.1 2.2 Jumping from height 103.0 Mesh Net 0.50 0 0 0 0 0 14.42 7 0.49 NaN NaN NaN NaN 9.58 3 0.31 NaN NaN NaN NaN NaN NaN NaN NaN
34 Ichikawa Study Japan NaN Railway NaN NaN NaN NaN Jumping in front of moving object NaN NaN NaN 0 0 0 0 1 7.00 79 11.29 NaN NaN 2008.0 4.0 3.00 53 17.67 NaN NaN NaN NaN NaN NaN NaN NaN
35 Matsubayashi Study Japan Tokyo Railway Metropolitan NaN NaN NaN Jumping in front of moving object NaN NaN NaN 0 0 0 0 1 10.76 127 11.80 NaN NaN 2008.0 3.0 0.24 1 4.17 NaN NaN No NaN NaN NaN NaN Poisson
36 Ueda Study Japan Tokyo Railway Metropolitan NaN NaN NaN Jumping in front of moving object NaN Platform Screen Doors 1.49 0 0 0 0 0 7.31 137 18.74 NaN NaN NaN NaN 2.69 7 2.60 NaN NaN No NaN NaN NaN NaN Poisson